Multi-view geometry theory is applied to atmospheric nuclear tests filmed in the 1950s and 1960s, to estimate the three-dimensional locations of fixed structures and radiological material during the detonation. Results show that using bundle adjustment, points can be estimated with an average uncertainty of 0.68 meters with 0.36, 0.28, and 0.37 meters of uncertainty in x-, y-, and z-directions ...

Luca Carlone1 [email protected] Pablo Fernandez Alcantarilla2 [email protected] Han-Pang Chiu3 [email protected] Zsolt Kira4 [email protected] Frank Dellaert1 [email protected] 1 Georgia Institute of Technology, College of Computing, USA 2 Toshiba Research Europe, Cambridge Research Laboratory, UK 3 SRI International, Center for Vision Technologies, US...

Robot deployment in open snow-covered environments poses challenges to existing vision-based localization and mapping methods. Limited field of view and over-exposure in regions where snow is present leads to difficulty identifying and tracking features in the environment. The wide variation in scene depth and relative visual saliency of points on the horizon results in clustered features with ...

Fast and reliable bundle adjustment is essential in many applications such as mobile vision, augmented reality, and robotics. Two recent ideas to reduce the associated computational cost are structure-less SFM (structure from motion) and incremental smoothing. The former formulates the cost function in terms of multi-view constraints instead of re-projection errors, thereby eliminating the 3D s...

In this paper we investigate the status of bundle adjustment as a component of a real-time camera tracking system and show that with current computing hardware a significant amount of bundle adjustment can be performed every time a new frame is added, even under stringent real-time constraints. We also show, by quantifying the failure rate over long video sequences, that the bundle adjustment i...

In this work we address robust estimation in the bundle adjustment procedure. Typically, bundle adjustment is not solved via a generic optimization algorithm, but usually cast as a nonlinear leastsquares problem instance. In order to handle gross outliers in bundle adjustment the least-squares formulation must be robustified. We investigate several approaches to make least-squares objectives ro...

Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras) is also sparse. The method, which we call Sp...

Bundle adjustment for multi-view reconstruction is traditionally done using the Levenberg-Marquardt algorithm with a direct linear solver, which is computationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop. This is appealing since the main computational step of the CG algorithm involves only a simple matrix-vector multiplicat...

This report derives a relative objective function for bundle adjustment – driven by the desire for a truly large scale simultaneous localization and mapping algorithm that can operate incrementally in constant time. It is precisely the choice of a single privileged coordinate frame that makes bundle adjustment expensive to solve. This is especially true during loop closures, when the single fra...